36 real-life Business Process Automation examples for Operations
36 real-life Business Process Automation examples for Operations
Automation is different in operations because the function sits at the intersection of systems, teams, and day-to-day execution, so value often comes from coordinating handoffs and exceptions rather than just automating one isolated task.
The examples across legal, private equity, charities, and manufacturing show that operations automation works best when it standardises repeatable steps, surfaces the right information faster, and reduces process friction across entire workflows rather than only speeding up individual actions.
Industry
Sector
EY's Automated Auditing System
EY has implemented a proprietary Robotic Process Automation (RPA) system that functions as a virtual assistant to streamline the auditing process, allowing the firm to automate the extraction of critical data from lease agreements and bank audit confirmations.
Use Case
Automating Auditing
Tools
Internal Tools
Input
Contracts, auditing files, and lease documents.
Process
The system replicates manual human workflows to read, review, and analyse document content. It automatically identifies and extracts key data points, such as commencement dates and payment terms. In practice, the AI handles approximately 70-80% of simple lease reviews and around 40% of complex real estate leases, flag-ging the remainder for human oversight.
Output
Automated acceptance and confirmation of audit requests, and the delivery of pre-processed documentation packages to auditors for final judgement.
Outcome
Significant reduction in administrative burden, with 50% of bank audit confirmations in EY Australia now processed via AI. This allows staff to focus on high-level analytical tasks and professional judgement rather than data entry.
Accounting, Audit and Tax
Operations
Amazon's Autonomous Checkout (Just Walk Out)
This multi-modal AI system by Amazon, known as Just Walk Out technology, utilises advanced computer vision and sensor fusion to enable a checkout-free shopping experience where customers simply exit the store to complete their purchase.
Use Case
Autonomous Checkout
Tools
Internal Tools & Amazon SageMaker
Input
Multi-view video feeds capturing shopper movement and weight sensor data from shelves to track small or similar-looking items.
Process
The system employs a multi-modal foundation model that processes video and sensor data through encoders, converting them into transformer tokens. This allows the model to interpret complex hand movements, differentiate between multiple shoppers in close proximity, and accurately track items picked up or returned to shelves. To ensure precision, the model is trained on over 10 auxiliary tasks including image segmentation, activity recognition, and 'grounding' (linking abstract concepts to physical objects).
Output
Real-time digital receipts that dynamically update as items are handled, providing a finalised bill to the shopper's Amazon account upon exit.
Outcome
Significant reduction in shopping time by eliminating checkout lines and the need for manual scanning. While Amazon has pivoted away from using the tech in its large-scale 'Fresh' grocery stores as of 2024-2025, the system remains a core B2B offering, operating in over 360 third-party locations including stadiums, airports, and hospitals globally by 2026.
Grocery and FMCG Retail
Operations
Due Diligence Automation in Private Equity and Venture Capital
Submitted by Frederic Kalinke
This AI Automation helps investment teams streamline due diligence by parsing prospective company documents to identify critical red flags, delivering a significant reduction in review time and increasing overall deal efficiency by 40% to 60%.
Ford Pro's Conversational Fleet Management Solution
Ford Pro AI is a conversational fleet management solution that enables operators to query their entire vehicle data ecosystem in real-time, transforming complex telematics into actionable summaries to reduce fuel costs, idle times, and administrative overhead.
Use Case
Fleet Management and Optimisation
Tools
Ford Pro AI
Input
Granular telematics data from connected vehicles, including fuel utilisation, idle times, speeding incidents, harsh acceleration, and unique fleet-specific historical data.
Process
The system rapidly extracts and distills massive datasets into simplified summaries. It conducts deep analyses of fleet behaviours and utilises conversational AI to process natural language queries. The tool is designed to work across mixed fleets, integrating data from both Ford and non-Ford vehicles through compatible telematics connections.
Output
On-demand summaries of vehicle health and performance, interactive visualisations of fleet trends, and specific recommendations for increasing operational efficiency.
Outcome
Enables fleet managers to answer complex vehicle-related questions in minutes rather than hours. By automating the administrative burden of data analysis, the tool allows managers to focus on high-value gains, such as reducing fuel expenditure and improving driver safety across more than 840,000 paid subscribers.
Schroders Capital has deployed a proprietary AI tool called "GAiiA" (Generative AI Investment Analyst) to automate the analysis of private equity investments. The system parses vast quantities of unstructured data to produce first drafts of investment memos, which are then refined by human investment professionals.
Use Case
Investment Memorandum Drafting
Tools
Internal Tools
Input
Financial statements, company filings, sell-side research, and news.
Process
The AI screens data across direct and co-investment opportunities, synthesising key information to answer a pre-set series of investment questions.
Output
First drafts of investment committee memoranda and responses to targeted due diligence enquiries.
Outcome
Enabled the team to assist with over 40 investment cases in its first year, significantly accelerating the due diligence timeline.
Asset and Wealth Management
Operations
Saudi Aramco's Oil Field Monitoring and Optimisation
This expansive AI monitoring system allows Saudi Aramco to oversee 500 oil fields simultaneously by processing data from 40,000 sensors, resulting in a 15% increase in production through real-time performance optimisation and predictive maintenance.
Use Case
Oil Field Monitoring and Optimisation
Tools
Internal Tools & IoT Sensors
Input
Real-time data streams from sensors monitoring wells, flares, reservoirs, and pipelines.
Process
The system continuously ingests and cleanses sensor data to monitor the health of equipment and overall field performance. It identifies well performance trends and utilises big data analytics to conduct predictive maintenance, flagging potential equipment failures or pressure build-ups before they occur.
Output
High-resolution reservoir models, real-time well performance reports, and automated recommendations for extraction optimisation.
Outcome
15% increase in total oil production and enhanced operational safety through proactive risk management.
Oil and Gas
Operations
Portfolio Performance Monitoring in Private Equity and Venture Capital
Submitted by Frederic Kalinke
This AI Automation helps Private Equity and Venture Capital companies streamline their data analysis for current and prospective portfolio companies by extracting and then aggregating data metrics from disparate data sources, saving considerable time and effort.
This automation enables a major refinery to minimise octane giveaway by utilising Canvass AI’s predictive modelling and simulation tools, leading to a significant annual saving of US$10 million and a drastic reduction in manual data processing.
Use Case
Reducing Octane Giveaway
Tools
Canvass AI
Input
Historical and real-time process data from blending operations, including feed compositions, temperatures, and flow rates.
Process
The system employs a prediction simulator to foresee product outcomes using diverse data sets without affecting the underlying AI model. It allows for 'what-if' analyses to experiment with process changes and uses machine learning to predict octane levels based on blend recipes and operating conditions.
Output
Accurate predictions of octane levels, recommended blend ratios, and dashboards comparing predicted versus actual octane performance.
Outcome
US$10 million annual savings and an 80% reduction in data wrangling time.
Oil and Gas
Operations
Mastercard's Transaction Risk Engine
Mastercard utilizes its Decision Intelligence technology to deploy sophisticated neural networks that evaluate transaction risk in real-time, allowing the company to distinguish between legitimate spending and fraudulent activity with unprecedented precision.
Use Case
Transaction Fraud Detection
Tools
Internal Tools
Input
Transaction details, merchant data, and cardholder patterns
Process
AI scores each transaction in real-time using neural networks trained on global payment data to predict fraud probability and intent
Output
Real-time approval or decline decisions
Outcome
Reduced false declines by 50% and prevented approximately $20 billion in annual fraud
Payments and Fintech
Operations
Zurich's Underwriting Assistant
Zurich North America has deployed a generative AI underwriting assistant, developed by InsurTech firm Sixfold, to automate the synthesis of complex commercial submissions. The tool allows underwriters to bypass the "manual hunt" for data by providing a high-fidelity first draft of the underwriting narrative, tailored specifically to the company’s unique risk appetite and internal formatting standards.
Use Case
Underwriting Narrative Automation and Document Summarisation
Tools
Sixfold
Input
Complex commercial insurance submissions, including broker emails, loss runs, risk reports, and exposure data spanning thousands of pages.
Process
The AI platform ingests the insurer’s specific underwriting guidelines and proprietary risk appetite. It then searches, classifies, and synthesises the submitted documents to identify key risk drivers and inconsistencies. Using Retrieval-Augmented Generation (RAG), it extracts relevant signals to generate a structured underwriting narrative that mimics the preferred tone and documentation standards of Zurich’s specialists.
Output
An AI-generated first draft of the underwriting narrative, risk scores (0–5) based on appetite alignment, and concise summaries of exposure and loss history.
Outcome
Underwriters save an average of 2 hours per submission, allowing them to process 80% of submissions with AI assistance during the initial phase. The saved time is reinvested into high-value broker negotiations and strategic decision-making. Following a successful pilot with 16 underwriters, the solution was expanded from four offices to dozens across the US within six months.
Insurance and Pensions
Operations
Allianz's Project Nemo Agentic Claims Settlement
Allianz deploy a multi-agent AI system in Australia to automate the end-to-end processing of low-complexity insurance claims, from coverage verification through to settlement, reducing processing times by 80%.
Use Case
Agentic Insurance Claims Processing
Tools
Internal Tools
Input
Customer-submitted claim details, policy data, and supporting documentation such as weather event records and purchase receipts.
Process
Seven specialised AI agents collaborate to plan, verify coverage, assess compliance, calculate settlement values, and communicate decisions without manual intervention.
Output
An automated claim decision and (where approved) a settlement payment issued directly to the claimant.
Outcome
Allianz achieve an 80% reduction in claim processing and settlement time, significantly improving customer satisfaction and freeing claims adjusters for complex cases.
Insurance and Pensions
Operations
FCA's Advanced Analytics Unit for Automated Scam Detection
The UK Financial Conduct Authority's Advanced Analytics Unit uses AI and web-scraping tools to automatically scan approximately 100,000 websites per day, identifying and triaging potential scam sites and fraudulent financial promotions at a scale impossible through manual review.
Use Case
AI-Automated Financial Scam and Fraud Detection
Tools
Internal Tools
Input
Public website content, social media posts, and financial promotion data scanned daily across the internet, alongside supervisory data and market surveillance feeds from regulated firms.
Process
Machine learning and web-scraping tools scan and classify online content for indicators of scam websites and unauthorised financial promotions, with predictive AI assisting supervision agents with real-time knowledge and an AI voice bot triaging incoming consumer contacts.
Output
Automated warnings published to the FCA Warning List, intelligence packages shared with law enforcement and financial institutions and flagged cases routed to human investigators for further action.
Outcome
The FCA scans 100,000 websites daily, a volume that could not be achieved manually, enabling more proactive disruption of fraud and supporting its 2025–2030 strategy to become a smarter, more data-driven regulator.
FedEx uses AI to plan and optimise 100,000 first-mile and last-mile delivery routes every day, analysing real-time traffic, weather, and delivery constraints to dynamically select the most efficient path for each shipment.
Use Case
AI-Driven Dynamic Delivery Route Optimisation
Tools
Internal Tools
Input
Real-time traffic data, weather feeds, road closure information, package characteristics, and historical delivery performance across FedEx's global network of 17 million daily packages.
Process
Machine learning models analyse multi-source data to generate dynamic routing recommendations and predict accurate delivery windows, adapting instantly to changing conditions and balancing middle-mile network load across aircraft and trucks.
Output
Optimised daily route plans for each driver and vehicle, with real-time re-routing instructions and accurate customer delivery ETAs.
Outcome
Delivery times reduced by up to 20%, with trailer utilisation increased by up to 13% and sorting accuracy above 99%, contributing to FedEx's target of AI integration across more than 50% of core operational workflows.
Road Freight and Last Mile
Operations
BASF's AI Soft Sensors for Real-Time Process Quality Monitoring
BASF deployed AI-powered soft sensors to continuously monitor quality parameters in real time, replacing periodic manual testing and enabling immediate process corrections.
Use Case
AI-Driven Real-Time Quality Parameter Monitoring
Tools
Internal Tools
Input
Continuous sensor data streams from production equipment, including temperature, pressure, flow rate, and feedstock composition readings across chemical production lines.
Process
Machine learning models trained on historical process and quality data analyse incoming sensor readings in real time, inferring quality parameters that would otherwise require costly lab tests and predicting deviation from specification.
Output
Real-time quality inferences and automated alerts issued to process engineers when parameters drift towards out-of-specification conditions.
Outcome
BASF reports a 30% reduction in batch defects and improved product consistency, without increasing testing costs or equipment investment.
Process Manufacturing
Operations
Bain's OpenAI Centre of Excellence for Client AI Delivery
Bain & Company establishes a dedicated OpenAI Centre of Excellence to co-design and deploy AI solutions for clients in retail and healthcare life sciences. Retail clients report improved inventory turnover and reduced operational costs, while life sciences clients achieve faster time-to-market and compliance efficiencies.
Use Case
AI-Enabled Client Solution Delivery at Scale
Tools
OpenAI & ChatGPT Enterprise
Input
Client business challenges in retail and healthcare life sciences, including inventory management, product launch timelines, and regulatory compliance requirements are scoped by Bain engagement teams.
Process
CoE-trained consultants apply OpenAI frontier models, combined with Bain's industry expertise, to design, build, and embed bespoke AI solutions into client operations and technology architectures.
Output
Deployed AI solutions integrated into client workflows, covering demand forecasting, compliance automation, process redesign and operating model transformation.
Outcome
Retail clients report improved inventory turnover and lower operational costs; life sciences clients accelerate time-to-market; AI and technology services now account for 30 percent of Bain revenue.
HSBC replaces its static rule-based transaction monitoring with a cloud-native AI platform built on Google Cloud AML AI, screening over one billion transactions per month to detect money laundering and financial crime with far greater accuracy than legacy systems.
Real-time transaction data from HSBC customers globally, including amounts, counterparties, geographies, and historical behavioural profiles.
Process
Supervised and unsupervised machine learning models analyse transaction patterns at scale on Google Cloud, learning criminal methodologies and adapting to new evasion techniques continuously.
Output
Risk-scored transaction alerts prioritised for compliance team investigation, with suppressed false positives and network-level criminal connection maps via Quantexa.
Outcome
The system detects two to four times more suspicious activity compared with prior methods, while reducing false positives by 60% with considerable savings in annual compliance review costs.
Retail and SME Banking
Operations
Hydro-Québec's deep neural network short-term load forecasting
Hydro-Québec deploys deep neural network models for real-time short-term electricity load forecasting, replacing legacy rule-based models that struggled to anticipate atypical demand behaviours during extreme weather events.
Use Case
AI-Powered Electricity Demand Forecasting
Tools
Internal Tools
Input
Historical electricity consumption data, real-time smart meter feeds, weather station data, and calendar and economic indicators.
Process
Deep neural networks trained on years of consumption and climate data continuously generate rolling short-term load forecasts, adapting dynamically to unusual demand patterns that confounded legacy statistical models.
Output
Continuous near-term electricity demand forecasts consumed by grid operators and generation dispatch teams for balancing and reserve management decisions.
Outcome
During a recent heatwave, the AI model correctly predicted the absence of a typical load decrease that the legacy model missed, avoiding the need for significant emergency operator corrections of 1,500MW.
Power Generation and Utilities
Operations
SBS Transit's AI Predictive Maintenance Across Bus Fleet
Singapore's SBS Transit deployed an AI-driven predictive maintenance system across its bus fleet, monitoring real-time health of brakes, engines, and battery packs through a centralised platform to reduce breakdowns and improve service reliability.
Use Case
AI-Powered Fleet Predictive Maintenance
Tools
IoT Sensors
Input
Real-time sensor data from brakes, engines, and battery packs streamed from buses in service across SBS Transit's fleet in Singapore.
Process
Machine learning models continuously analyse sensor streams to detect early indicators of mechanical degradation, generating condition scores and alerting maintenance teams to specific components requiring intervention before failure occurs.
Output
Prioritised maintenance alerts and recommended service schedules delivered to maintenance teams via a centralised AI platform, with component-level fault identification.
Outcome
A 20% reduction in bus breakdowns achieved in the initial phase of 1,000 buses, prompting expansion to over 3,000 buses covering approximately 62% of the fleet.
Rail, Metro and Bus Operators
Operations
UPS's ORION Delivery Route Optimisation
UPS deploy ORION, an AI-powered route optimisation system, across 55,000 US drivers to calculate the most fuel-efficient delivery sequences daily, combining prescriptive analytics with real-time traffic and package data.
Use Case
AI-Powered Last-Mile Route Optimisation
Tools
Internal Tools
Input
Package delivery data, GPS telemetry, traffic conditions, driver schedules, and 250 million address data points processed for each driver each day.
Process
A proprietary operations research algorithm evaluates over 200,000 routing options per driver, dynamically adjusting sequences during the day based on live conditions.
Output
An optimised daily delivery sequence for each driver, updated in real time via in-cab devices.
Outcome
UPS save 100 million miles driven annually, reduce fuel use by 10 million gallons and achieve $300–400 million in annual cost savings while cutting CO₂ emissions by 100,000 metric tons.
Logistics and Warehousing
Operations
BHP's Azure Machine Learning for Copper Concentrator Optimisation
BHP implemented Azure Machine Learning to provide real-time operational recommendations for concentrator operations, improving copper recovery rates and generating measurable production uplift.
Use Case
AI-Powered Mineral Processing Optimisation
Tools
Azure Machine Learning
Input
Real-time sensor and operational data from the Escondida concentrator plant, including ore feed characteristics, reagent dosing, flotation cell performance and plant throughput metrics.
Process
Azure Machine Learning models analyse incoming process data and compare it to historical performance patterns, generating real-time recommendations for operators to adjust reagent dosing, grind size and flotation parameters to maximise copper recovery.
Output
Real-time operational guidance delivered to plant operators, recommending specific process adjustments to optimise concentrate grade and recovery within current operating conditions.
Outcome
BHP reports an operational uplift of $18.9 million from improved copper recovery rates at the Escondida site, leading the company to launch its first Industry AI Hub to scale predictive analytics across its global supply chain.
Mining and Commodities
Operations
Vestas's AI-Driven Turbine Health Monitoring and Predictive Maintenance
Vestas uses machine learning to continuously monitor wind turbine component health from control sensors across its global installed base, predicting wear and performance issues before they result in unscheduled downtime.
Use Case
AI Predictive Maintenance for Wind Turbines
Tools
IoT Sensors
Input
Continuous data streams from control sensors monitoring vibration, temperature, acoustics and performance parameters across Vestas turbines operating globally.
Process
Machine learning models trained on sensor data and maintenance records analyse real-time streams to identify subtle patterns indicative of bearing wear, blade imbalance, or component degradation with self-calibrating sensors that adapt to each turbine type and site.
Output
Condition health scores and specific maintenance recommendations provided to service teams, specifying which components require intervention and when, enabling condition-based rather than time-based servicing.
Outcome
Vestas reports the ability to predict failure signatures weeks in advance, reducing unscheduled downtime and optimising maintenance scheduling across turbines in over 80 countries.
Renewables
Operations
National Grid ESO's AI solar generation nowcasting
National Grid ESO partners with Open Climate Fix to deploy an AI solar nowcasting system that produces highly accurate short-term forecasts of solar generation output, enabling control room operators to reduce expensive backup gas plant kept on standby.
Use Case
AI Solar Generation Short-Term Forecasting
Tools
Open Climate Fix
Input
Real-time satellite imagery of cloud cover, historical solar generation data, and weather station feeds covering the GB electricity network.
Process
AI models train on satellite image sequences to track cloud movement and predict solar irradiance levels, translating outputs into generation forecasts for the transmission system operator control room.
Output
Accurate near-term solar generation forecasts updated continuously, consumed directly by grid balancing and dispatch teams.
Outcome
Improved solar nowcasting gives grid operators greater confidence to reduce idling gas reserve capacity, saving millions in balancing costs and reducing carbon emissions from unnecessary standby generation.
Power Generation and Utilities
Operations
Wagestream's AI Customer Support
Wagestream has revolutionised its internal support infrastructure by deploying Gemini models on Google Cloud to automate the resolution of routine employee inquiries. By integrating real-time account data and historical ticket grounding, the system independently manages queries regarding pay dates and balances, allowing human support agents to dedicate their expertise to high-value, complex problem-solving.
Use Case
Automated Customer Support Resolution
Tools
Google Gemini
Input
Customer support queries, employee account data, pay schedules, balance information, and historical ticket data
Process
Gemini models process queries via API integration with account systems to understand and resolve routine issues, while maintaining an automated escalation path for complex cases
Output
An always-on AI support layer capable of handling the majority of routine inquiries and a scalable infrastructure for rapid growth
Outcome
Over 80% of internal inquiries handled by AI, leading to a significant reduction in workload and faster resolution times
Payments and Fintech
Operations
CareSource Automates Healthcare Document Processing with UiPath
CareSource, a US-based managed care organisation serving over two million members, used UiPath intelligent automation to redesign how it processed large volumes of critical healthcare documents including claims, prior authorisations, faxes, and invoices. The automation reduced manual intervention across its Claims, Utilisation Management, and Clinical Management teams.
Use Case
Automated document processing and claims management across healthcare operations
Tools
UiPath
Input
Incoming healthcare documents including claims, prior authorisation requests, faxes, and vendor invoices received by the C/UM/CM team
Process
UiPath robots extract and classify document data using AI-powered Document Understanding, automatically route claims and authorisation requests, process invoices, and update relevant systems — reducing or eliminating manual review steps
Output
Processed claims, approved prior authorisations, validated invoices, and updated clinical records delivered with reduced manual touchpoints and faster turnaround times
Outcome
Improved claim processing efficiency with reduced manual intervention; enhanced prior authorisation and invoice handling with fewer delays; transformed clinical operations with improved accuracy. CareSource was named a 2024 UiPath AI25 Award winner.
Hospitals and Clinics
Operations
Allianz's Incognito Insurance Fraud Detection
Allianz deploy a supervised machine learning system called Incognito to detect fraud in motor and home insurance claims by analysing distortions in images, videos, and documents submitted with applications.
Use Case
Automated Insurance Fraud Detection
Tools
Internal Tools
Input
Claims submissions including photographs, video evidence, scanned documents, and application data across motor and home insurance lines.
Process
A supervised machine learning model analyses visual and documentary artefacts for anomalies and distortions that indicate manipulation or fraudulent staging.
Output
Flagged claims and applications identified as potentially fraudulent, routed for specialist investigator review.
Outcome
Allianz UK have achieved a 29% increase in fraud detection, saving £37.7 million and increasing application fraud detection by 150% against their target.
Insurance and Pensions
Operations
Bosch's AI-Powered Optical Quality Inspection
Bosch deploy computer vision and deep learning across 50 manufacturing plants to automate optical component inspection, anomaly detection, and root cause analysis, supporting a company-wide zero-defect production target.
Use Case
Automated Optical Defect Detection
Tools
Internal Tools
Input
High-resolution visual feeds and sensor data from production lines across 50 plants, covering components in automotive, consumer goods, and industrial equipment manufacturing.
Process
Deep learning models trained on labelled defect imagery analyse production-line camera feeds in real time, flagging anomalies and triggering root cause analysis workflows.
Output
Real-time defect alerts, anomaly classifications, and automated maintenance or production adjustment triggers delivered to plant operators.
Outcome
Bosch achieve measurable reductions in defect escape rates across their manufacturing estate, progressing towards their stated commitment that every Bosch product is either AI-enabled or manufactured using AI.
Discrete Manufacturing
Operations
Balfour Beatty's AI Inspection and Test Plan Agent
Balfour Beatty use AI agents on infrastructure projects to automate the review of inspection and test plans, identifying outdated or incorrect templates before they reach technical experts.
Use Case
Automated Quality Assurance Document Review
Tools
Microsoft 365 Copilot
Input
Inspection and test plan documents submitted across infrastructure projects, including templates, revision histories, and compliance checklists.
Process
An AI agent automatically reviews submitted inspection and test plans, flags documents using outdated or incorrect templates, and routes validated versions to engineers.
Output
Reviewed inspection and test plans with identified inconsistencies flagged, freeing engineers from manual document checking.
Outcome
Balfour Beatty accelerate project delivery and allow engineers to focus on design assurance and technical problem-solving, automating a manual review process previously repeated thousands of times company-wide.
Construction and Contractors
Operations
Georgia State University's Pounce AI Student Services Chatbot
Georgia State University deploys Pounce, an AI-powered chatbot, to handle hundreds of thousands of student enquiries annually, covering admissions, financial aid, and enrolment, which frees staff for complex advisory work and contributing to improved student retention rates.
Use Case
Automated Student Services and Enquiry Handling
Tools
Internal Tools
Input
Student queries submitted via text and web channels, covering admissions status, financial aid, course registration and campus services
Process
A conversational AI interprets student queries, retrieves answers from institutional knowledge bases, and escalates complex cases to human advisors.
Output
Instant, accurate responses to student enquiries around the clock with automated escalation routing for complex cases
Outcome
Hundreds of thousands of queries handled without human intervention, freeing staff for higher-value advisory work and improving student retention.
Schools and Universities
Operations
Impact for Women Automates Charitable Delivery Logistics with OutSystems
This automation enables Impact for Women to streamline the logistics of their 'Bags of Love' programme, utilising a custom OutSystems low-code application to manage complex delivery allocations. By replacing over 60 hours of manual spreadsheet and email coordination with an automated dashboard, the charity has significantly reduced administrative overhead and established a scalable digital foundation to support its national expansion.
Use Case
Automating charitable delivery allocation
Tools
OutSystems
Input
Agency delivery requests, volunteer driver availability, and donation inventory data previously managed via spreadsheets and manual emails
Process
A custom OutSystems application centralises resource allocation via a dashboard. The app recommends available volunteer drivers, allocates deliveries, and automatically sends email confirmations to both drivers and receiving agencies.
Output
Automated delivery assignments, email notifications to volunteer drivers and partner agencies, and a centralised dashboard for managing all delivery operations.
Outcome
Admin time reduced by over 40 hours; key person dependency eliminated; deployed in 12 weeks to meet a pre-Christmas deadline; scalable foundation supports the charity's goal of going national.
Charities and NGOs
Operations
Helse Vest Deploys RPA to Reduce Clinical Admin Burden for Doctors and Nurses
Helse Vest, a regional health authority in Norway, deployed UiPath RPA (nicknamed 'Robbie Vest') to automate repetitive clinical data entry tasks across its hospitals. Key processes automated include prostate cancer patient data registration across three separate systems (DIPS, a research database, and reporting tools) and midwifery intake forms for pregnant women — replacing paper forms and manual multi-system entry.
Use Case
Automation of clinical data entry and patient record management across multiple hospital systems
Tools
UiPath
Input
Patient clinical data including prostate cancer records requiring entry into multiple systems, and digital intake form data from pregnant women submitted via the national health portal helsenorge.no
Process
UiPath robots automatically read patient data from source systems and replicate it across DIPS journal system, research databases, and reporting tools — eliminating duplicate manual entry. For midwifery, the robot captures digital form submissions and populates all required hospital systems automatically.
Output
Accurately populated patient records across all required clinical systems, with no manual re-entry; complete midwifery intake data available to midwives before birth
Outcome
Significant reduction in time spent on administrative data entry for doctors and nurses; improved accuracy of patient data in clinical systems; midwives now receive complete patient information before births, improving patient safety. Helse Vest has become a leading proponent of RPA within Norwegian healthcare.
Hospitals and Clinics
Operations
Lemonade's Jim AI Claims Agent
Lemonade deploy Jim, an AI claims agent within their mobile-first insurance platform, to handle over 30% of all incoming claims autonomously, with the fastest cases resolved and paid out within seconds of submission.
Use Case
Autonomous Insurance Claims Resolution
Tools
Internal Tools
Input
Customer-submitted claims via the Lemonade app, including incident descriptions, supporting photos, and policy details.
Process
The Jim AI agent assesses claim validity against policy terms, runs anti-fraud checks using behavioural signals and cross-referencing, and approves or declines claims without human review where confidence thresholds are met.
Output
An instant claim decision with automated payment processing for approved claims, or a flagged case routed to human review.
Outcome
Lemonade handle more than 30% of claims without any human involvement, with some claims paid in under three seconds, enabling a scalable operational model without proportional headcount growth.
Insurance and Pensions
Operations
Malaysia's AI at Work 2.0 Generative AI Deployment for Civil Servants
The Malaysian Ministry of Digital partnered with Google Cloud to deploy generative AI tools including Gemini and NotebookLM to 445,000 public officers, enabling civil servants to automate administrative tasks and improve public service quality.
Use Case
Generative AI-Powered Civil Service Productivity
Tools
Google Gemini
Input
Civil servant administrative tasks including document drafting, information retrieval, report summarisation and citizen query handling across Malaysian government departments.
Process
Google Cloud AI tools including Gemini provide civil servants with conversational AI assistance for drafting, summarising, and analysing documents, while NotebookLM enables officers to query and synthesise large document sets rapidly.
Output
AI-generated document drafts, summaries, and responses produced for civil servants, reducing time spent on routine administrative work and freeing capacity for higher-value public service delivery.
Outcome
Early pilots showed 97% of users saving time, on average 3.25 hours per week, while improving the quality of their work and contributing to a digitally empowered civil service at national scale.
Central Government
Operations
John Deere's See & Spray precision weed targeting
John Deere deploys AI-powered computer vision across spray booms to identify and selectively target weeds in real time, dramatically reducing herbicide consumption and improving crop yield for farmers.
Use Case
Precision Herbicide Application
Tools
Internal Tools
Input
Live video feeds from 36 boom-mounted cameras scanning over 2,100 square feet of cropland per second.
Process
Deep learning models trained on millions of crop and weed images classify each plant species in real time and trigger individual nozzles only where weeds are detected.
Output
Targeted herbicide application maps and weed pressure reports delivered to operators via the John Deere Operations Centre.
Outcome
In one season, See & Spray covered five million acres and saved farmers an estimated 31 million gallons of herbicide mix, improving yields by up to two bushels per acre.
Farming and Agribusiness
Operations
easyJet's Skywise AI predictive maintenance programme
easyJet partners with Airbus and Palantir to deploy the Skywise predictive maintenance platform across its 350-aircraft A320 family fleet, shifting from reactive repairs to data-driven component intervention before failures occur.
Use Case
Predictive Aircraft Component Failure Detection
Tools
Airbus Skywise & Palantir
Input
Up to 24,000 flight parameters per journey streamed in real time from onboard FOMAX data-capture units across the entire fleet.
Process
Machine learning models analyse sensor data against historical component behaviour and OEM tolerances to generate early-warning alerts for specific component degradation.
Output
Automated maintenance alerts with recommended intervention timelines, weed pressure-style wording, and spare parts reservations pushed to the Maintenance Control Centre.
Outcome
Between 2019 and 2025 easyJet avoided 1,343 flight cancellations and 171 major delays, with components removed via predictive alerts showing a 5% lower no-fault-found rate than reactive removals.
Airlines and Air Cargo
Operations
American Express's Gen X Real-Time Fraud Model
American Express deploy their tenth-generation machine learning fraud model, Gen X, to evaluate every credit card transaction in real time across more than 8 billion annual transactions, maintaining the lowest fraud rates in the industry.
Use Case
Real-Time Transaction Fraud Scoring
Tools
NVIDIA AI Platform
Input
Every American Express transaction globally (over 8 billion per year) along with cardholder history, merchant data, and contextual behavioural signals.
Process
A deep learning model executes a sequence of more than 1,000 decision trees, generating a fraud decision within two milliseconds.
Output
A real-time fraud risk score for each transaction, triggering automated approval, denial, or pending review.
Outcome
American Express maintain fraud rates approximately half those of their competitors, protecting over $1.2 trillion in annual card spend.
Payments and Fintech
Operations
SOP Documentation Automation at Eaton Using Microsoft 365 Copilot
Eaton, a global power management company, used Microsoft 365 Copilot to automate the creation of standard operating procedures (SOPs) during a major accounting centralisation programme. The finance team needed to document 9,000 SOPs within a strict timeline; Copilot reduced SOP creation time by 83%, from over an hour each to just 10 minutes.
Use Case
Finance process documentation and SOP creation automation
Tools
Microsoft 365 Copilot
Input
Existing process knowledge held by finance team members, prior SOP drafts, and internal organisational data accessible via Microsoft 365
Process
Finance staff use Microsoft 365 Copilot to generate initial SOP drafts by prompting the tool with process descriptions. Copilot draws on internal documents and Microsoft Graph data to produce structured, formatted SOPs ready for review. Copilot for Service is additionally used to surface CRM insights and speed up customer support response times.
Output
Over 1,000 completed SOP documents generated with AI assistance; faster customer service response times via CRM-integrated Copilot for Service
Outcome
SOP creation time dropped from over 60 minutes to 10 minutes per document (83% reduction); more than 650 hours saved across the SOP programme; customer service response times projected to fall by 20%; finance teams freed to focus on strategic work rather than manual documentation
